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Deployment-Efficient Reinforcement Learning via Model-Based Offline Optimization

Tatsuya Matsushima, Hiroki Furuta, Yutaka Matsuo, Ofir Nachum, Shixiang Gu

arXiv:2006.03647v2cs.LGcs.AIstat.ML

TL;DR

Frequent policy deployment makes RL costly and risky, while existing offline methods often struggle with randomly initialized policies and smaller datasets. BREMEN combines model ensembles, imagined rollouts, behavior-cloned initialization, and trust-region updates to optimize policies offline. It learns successful policies with 5-10 deployments and supports datasets 10-20 times smaller than prior works, while fully safe learning from scratch remains open.

  • Problem

    RL methods often require frequent policy deployments, while existing offline approaches can fail from random initialization and are not effective on smaller datasets.

  • Method

    BREMEN learns an ensemble of dynamics models with imaginary rollouts and implicitly regularizes policy updates through behavior-cloned initialization and conservative trust-region learning.

  • Results

    BREMEN learns successful policies from scratch with 5-10 deployments and can learn with datasets 10-20 times smaller than prior methods.

  • Takeaways & Limitations

    Deployment efficiency provides a criterion for reducing the costs and risks of RL policy deployment while retaining effective policy learning in the studied settings.

  • Takeaways & Limitations

    BREMEN still requires a few online deployments, and fully safe and efficient RL that learns from scratch remains an open problem.

Abstract

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Most reinforcement learning (RL) algorithms assume online access to the environment, in which one may readily interleave updates to the policy with experience collection using that policy. However, in many real-world applications such as health, education, dialogue agents, and robotics, the cost or potential risk of deploying a new data-collection policy is high, to the point that it can become prohibitive to update the data-collection policy more than a few times during learning. With this view, we propose a novel concept of deployment efficiency, measuring the number of distinct data-collection policies that are used during policy learning. We observe that naïvely applying existing model-free offline RL algorithms recursively does not lead to a practical deployment-efficient and sample-efficient algorithm. We propose a novel model-based algorithm, Behavior-Regularized Model-ENsemble (BREMEN) that can effectively optimize a policy offline using 10-20 times fewer data than prior works. Furthermore, the recursive application of BREMEN is able to achieve impressive deployment efficiency while maintaining the same or better sample efficiency, learning successful policies from scratch on simulated robotic environments with only 5-10 deployments, compared to typical values of hundreds to millions in standard RL baselines. Codes and pre-trained models are available at https://github.com/matsuolab/BREMEN .

1 Introduction

Deployment efficiency measures how often data-collection policies change, addressing the costs and risks of frequent deployment. The paper introduces BREMEN to support offline policy improvement with few deployments and smaller datasets.

  • Motivation: Real-world RL applications face costs and risks when deploying new data-collection policies, making fewer deployments valuable.Examples include health, education, dialogue agents, and robotics.
  • Motivation: Deployment efficiency counts changes in the data-collection policy, complementing sample efficiency, which measures environment interactions.The distinction matters because algorithms can use few samples while still requiring many policy deployments.
  • Motivation: Naïvely applying model-free offline RL recursively is ineffective for achieving both sample efficiency and deployment efficiency on smaller datasets.Existing methods are often tuned for datasets containing about one million transitions.
  • Contribution: BREMEN combines model-based learning, imaginary rollouts, behavior regularization, policy initialization, and conservative trust-region updates.The method learns an ensemble of dynamics models jointly with the policy.
  • Results: BREMEN learns successful policies with 5-10 deployments and learns from datasets 10-20 times smaller than prior methods in fixed-batch settings.On standard offline benchmarks, it remains competitive with state-of-the-art performance at standard dataset sizes.

2 Preliminaries

The paper formulates RL using a Markov Decision Process with states, actions, transitions, rewards, and discounting. Model-based RL estimates unknown dynamics with a parameterized model, while online and offline distinctions concern how experience is collected and reused.

  • MDP formulation: An MDP is represented by states, actions, transition dynamics, rewards, and a discount factor, with policies mapping states to action distributions.The objective is to obtain an optimal policy maximizing expected discounted rewards.
  • MDP formulation: The transition distribution is generally unknown and is estimated by a parameterized dynamics model in model-based RL.The paper assumes known rewards for simplicity but notes that rewards can also be modeled.
  • Online and offline RL: On-policy methods discard data after updates, whereas off-policy methods accumulate experience in a replay buffer.Both remain online RL when they require repeated deployments during learning.

3 Deployment Efficiency

Deployment efficiency counts the number of data-collection policy changes during learning, separating deployment burden from total sample use. The proposed target lies between one-deployment offline RL and hundreds-to-millions-deployment online RL.

  • Definition: Deployment efficiency is the number of times the data-collection policy changes while learning from a random policy to a solution.Lower values indicate greater deployment efficiency.
  • Definition: If each of I deployments collects B transitions, deployments equal I while total samples equal I × B.Sample efficiency therefore measures a different axis from deployment efficiency.
  • Deployment regimes: Online RL typically performs hundreds to millions of deployments, whereas offline RL commonly studies a single deployment.A deployment-efficient method should occupy the practical middle ground while learning successfully from scratch.
  • Prior benchmarks: SAC performs half a million to one million policy deployments on MuJoCo benchmarks, while ME-TRPO performs 100-300.The paper reports successful learning with only 5-10 deployments.

4 Behavior-Regularized Model-Ensemble

BREMEN uses an ensemble dynamics model, imagined rollouts, behavior-cloned initialization, and conservative trust-region updates to improve policies while controlling distribution shift. Its analysis links these choices to implicit KL regularization and model-based improvement guarantees.

  • Model learning and rollouts: BREMEN learns an ensemble of dynamics models and a policy using Dyna-style imaginary rollouts.The ensemble is intended to alleviate model bias, while each imagined trajectory samples models during rollout.
  • Model learning and rollouts: Each dynamics model minimizes mean squared prediction error between predicted and true next states over the dataset.This trains the learned transition models used for policy optimization.
  • Behavior regularization: After each deployment, BREMEN behavior-clones the collected data, initializes the target policy from that clone, and applies finite KL-based trust-region updates.These choices address discrepancy between true dynamics and learned models under distribution shift.
  • Empirical result: The paper reports that its regularization technique outperforms an explicit KL penalty in empirical comparisons.This result is reported for the behavior-regularization comparison.
  • Deployment-efficient learning: Recursive offline application starts from a random policy, collects experience, and performs offline policy updates between deployments.Algorithm 1 repeats data collection, model fitting, behavior cloning, policy reinitialization, and imagined-rollout optimization.
  • Theory: The theoretical bound guarantees true-return improvement when model-return improvement exceeds slack from model and policy distribution errors.The analysis connects this condition to dynamics learning, behavior-cloned initialization, and conservative trust-region updates.

5 Experiments

The experiments evaluate BREMEN in limited-deployment and static offline settings against model-based, model-free, and recursively applied offline baselines. BREMEN learns successfully with few deployments and remains effective on substantially smaller datasets, while its implicit regularization keeps policies near deployed behavior.

  • Experimental setup: BREMEN is evaluated from scratch on Ant, HalfCheetah, Hopper, and Walker2d in limited-deployment and static offline settings.The deployment-constrained comparisons include ME-TRPO, SAC, BCQ, and BRAC.
  • Deployment efficiency: 5-10 deployments suffice for BREMEN to learn successful policies, whereas SAC online may require 100k-500k deployments.SAC online is substantially more sample-efficient, but uses one deployment per sample.
  • Deployment efficiency: With 200,000 or 100,000 transitions per deployment, BREMEN performs strongly while existing online and offline baselines often make little progress.The comparison aligns deployment counts and data collected per deployment across methods.
  • Offline learning: On standard 1M-transition datasets, BREMEN achieves performance competitive with state-of-the-art model-free offline methods.The static-dataset comparison reports average cumulative rewards and standard deviations for 1M, 100K, and 50K samples.
  • Offline learning: 10-20 times smaller datasets still allow recursive BREMEN to learn appropriately, while BCQ and BRAC fail to exceed the behavioral-cloning baseline.This supports both deployment efficiency and sample efficiency for recursive BREMEN.
  • Implicit regularization: Behavior initialization keeps the improving policy close to the last deployed policy without an explicit value KL penalty.Variants using only an explicit KL penalty move farther from the last deployed policy.

6 Related Work

The related work positions deployment efficiency alongside offline RL and model-based RL, while distinguishing BREMEN’s implicit behavior regularization and recursive evaluation. It combines behavior-cloned initialization, conservative trust-region updates, and model ensembles to address policy shift and model bias.

  • Deployment Efficiency and Offline RL: Offline RL studies an extreme one-deployment setting, but such methods struggle when learning from scratch or when datasets are small.Prior offline methods commonly use static data collected with a partially trained policy.
  • Deployment Efficiency and Offline RL: BREMEN implicitly regularizes its target policy through behavior-cloned initialization followed by conservative trust-region updates rather than an explicit behavior-policy constraint.The approach draws inspiration from behavior regularization and uncertainty-handling ensembles.
  • Deployment Efficiency and Offline RL: The paper distinguishes its work by applying model-based methods in fully offline settings and by defining and extensively evaluating deployment efficiency recursively.It presents this as a difference from prior offline model-free work and related concurrent model-based studies.
  • Model-Based RL: Model-based RL can suffer model bias when policies exploit regions unsupported by data, motivating ensembles and trust-region updates.BREMEN combines these remedies with behavior-cloned initialization to alleviate distribution shift.

7 Conclusion

The paper introduces deployment efficiency and BREMEN to learn successful policies from scratch under severe deployment limits and smaller data batches. It argues that this can reduce deployment costs and risks while supporting communication-efficient distributed reinforcement learning.

  • Contribution: Deployment efficiency counts changes in the data-collection policy during learning.It complements sample efficiency, which measures environment interactions rather than policy changes.
  • Contribution: BREMEN uses implicit KL regularization through policy initialization and trust-region updates in a model-based offline algorithm.The method learns successful policies from scratch in only 5-10 deployments.
  • Results: BREMEN can improve policies with batch sizes 10-20 times smaller than prior works while maintaining deployment-efficient learning.The paper presents this as reducing costs and risks in real-world applications.
  • Implications: The approach could reduce communication required during distributed learning and support communication-efficient large-scale reinforcement learning.This is presented as a potential consequence of reducing deployment frequency.
  • Conclusion: Under deployment-efficiency constraints, most prior model-free, model-based, online, and offline algorithms fail to achieve successful learning.The conclusion frames deployment efficiency as a criterion for safe and practical reinforcement learning.

Broader Impact

Deployment efficiency matters in real-world RL because repeated policy deployments can be harmful or costly. BREMEN reaches 5-10 deployments on standard MuJoCo benchmarks, though it can underperform deployment-unconstrained SAC and still entails some risk.

  • 5-10 deployments enable BREMEN to learn standard OpenAI Gym MuJoCo benchmarks, which the authors state no other algorithms can match.
  • Deployment efficiency is relevant when excessive policy deployments may be harmful or costly in robotics, health care, dialogue agents, and education.
  • BREMEN’s final performance is sometimes worse than deployment-unconstrained SAC.
  • BREMEN still requires a few online deployments, which may involve risks, so fully safe and efficient RL remains an open problem.

A Proof of Proposition 1

The proof begins from the behavior-cloning objective and applies Pinsker’s inequality to policy and dynamics terms before combining the resulting bounds with the triangle inequality.

  • The proof first considers ϵπ through the behavior-cloning objective in supremum form.
  • Pinsker’s inequality is applied to the true and estimated behavior policy to obtain a bound.
  • The proof combines the intermediate bounds using the triangle inequality.
  • A further Pinsker inequality is used for the true dynamics and learned model.

B.1 Implementation Details

The experiments use MuJoCo continuous-control benchmarks and compare BREMEN with established offline and model-based baselines under deployment-efficient and offline settings. Implementation choices include behavior initialization, conservative updates, stationary Gaussian noise, and dynamics-model ensembles.

  • Experimental setup: Experiments use four standard MuJoCo environments, with reward and termination functions modified for neural-network dynamics rollouts.The environments are Ant, HalfCheetah, Hopper, and Walker2d; Ant excludes contact information from observations.
  • Deployment-efficient settings: Policy-optimization iterations between deployments were selected separately: BREMEN uses 2,000 iterations in Ant, HalfCheetah, and Walker2d, and 6,000 in Hopper.The search considered {2,000, 4,000, 6,000}; SAC and other baselines used their own searched iteration counts.
  • Exploration noise: Stationary zero-mean Gaussian noise with scale 0.1 was selected after comparing stationary and learned noise strategies in HalfCheetah.The same stationary-noise choice was used for all experiments.
  • Offline settings: BREMEN uses 6,250 offline policy-optimization iterations, whereas BC, BCQ, and BRAC use 500,000.Offline experiments use one fixed batch dataset, while deployment-efficient experiments recursively collect data and optimize offline.
  • BREMEN procedure: BREMEN trains dynamics-model ensembles and a behavior-cloned policy before optimizing the target policy with imaginary rollouts.The offline procedure trains K dynamics models, estimates the behavior policy by cloning, initializes the target policy from it, and performs iterative rollout-based optimization.
  • Results and design choices: BREMEN remains competitive with state-of-the-art model-free offline RL on noisy datasets, while behavior initialization and conservative trust-region updates provide implicit KL regularization.More ensemble models generally improve performance by mitigating distribution shift, except at K = 10; the selected ensemble size is K = 5.

C.5 Deployment-Efficient RL Experiment with Different Reward Function

The study evaluates BREMEN with a modified HalfCheetah reward function resembling the DeepMind Control Suite cheetah-run task. BREMEN retains better deployment efficiency than most compared methods in this setting, except SAC.

  • Environment and reward: The HalfCheetah environment was modified to resemble the DeepMind Control Suite cheetah-run task, using a different reward function and no termination.Figure 17 evaluates BREMEN and existing methods under this modified setting.
  • Comparison: BREMEN shows better deployment efficiency than existing offline methods and online ME-TRPO, with SAC as the exception.The passage reports the same trend as in the main results.
  • Comparison: Figure 17 compares BREMEN, ME-TRPO(offline), BC, BCQ, BRAC, and BRAC(max Q) in the modified HalfCheetah setting.The comparison concerns performance under the alternate reward function.
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